A lot of stuff working :D. ResNet implemented!

2 files changed, 639 insertions, 25 deletions

diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index 49ca4c4..3f008c3 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -2,11 +2,120 @@
  "cells": [
   {
    "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import torch\n",
+    "from importlib.util import find_spec\n",
+    "if find_spec(\"text_recognizer\") is None:\n",
+    "    import sys\n",
+    "    sys.path.append('..')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.nn.modules.activation.SELU"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.nn.SELU"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch"
+    "a = \"nNone\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = a or \"relu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'nnone'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'nNone'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
    ]
   },
   {
@@ -986,28 +1095,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'tqdm.auto.tqdm'; 'tqdm.auto' is not a package",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-20-68e3c8bf3e1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtqdm_auto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tqdm.auto.tqdm'; 'tqdm.auto' is not a package"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import tqdm.auto.tqdm as tqdm_auto"
+    "import tqdm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1016,7 +1113,7 @@
        "tqdm.notebook.tqdm_notebook"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1027,25 +1124,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tqdm.auto.tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
     "def test():\n",
-    "    for i in range(9):\n",
+    "    for i in tqdm.auto.tqdm(range(9)):\n",
     "        pass\n",
-    "    print(i)"
+    "    print(i)\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1d3b25d4ee141e882e316ec54e79d60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
       "8\n"
      ]
     }
@@ -1056,6 +1178,479 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import sleep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41b743273ce14236bcb65782dbcd2e75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pbar = tqdm.auto.tqdm([\"a\", \"b\", \"c\", \"d\"], leave=True)\n",
+    "for char in pbar:\n",
+    "    pbar.set_description(\"Processing %s\" % char)\n",
+    "#     pbar.set_prefix()\n",
+    "    sleep(0.25)\n",
+    "pbar.set_postfix({\"hej\": 0.32})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pbar.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb5ad8d6109f4b1495b8fc7422bafd01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tqdm.auto.tqdm(total=10, bar_format=\"{postfix[0]} {postfix[1][value]:>8.2g}\",\n",
+    "          postfix=[\"Batch\", dict(value=0)]) as t:\n",
+    "    for i in range(10):\n",
+    "        sleep(0.1)\n",
+    "#         t.postfix[2][\"value\"] = 3 \n",
+    "        t.postfix[1][\"value\"] = i / 2\n",
+    "        t.update()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b341d49ad074823881e84a538bcad0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tqdm.auto.tqdm(total=100, leave=True) as pbar:\n",
+    "    for i in range(2):\n",
+    "        for i in range(10):\n",
+    "            sleep(0.1)\n",
+    "            pbar.update(10)\n",
+    "            pbar.set_postfix({\"adaf\": 23})\n",
+    "        pbar.set_postfix({\"hej\": 0.32})\n",
+    "        pbar.reset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, Encoder, ResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "IdentityBlock(\n",
+       "  (blocks): Identity()\n",
+       "  (activation_fn): ReLU(inplace=True)\n",
+       "  (shortcut): Identity()\n",
+       ")"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IdentityBlock(32, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ResidualBlock(\n",
+       "  (blocks): Identity()\n",
+       "  (activation_fn): ReLU(inplace=True)\n",
+       "  (shortcut): Sequential(\n",
+       "    (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ResidualBlock(32, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BasicBlock(\n",
+      "  (blocks): Sequential(\n",
+      "    (0): Sequential(\n",
+      "      (0): Conv2dAuto(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): Sequential(\n",
+      "      (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (activation_fn): ReLU(inplace=True)\n",
+      "  (shortcut): Sequential(\n",
+      "    (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "dummy = torch.ones((1, 32, 224, 224))\n",
+    "\n",
+    "block = BasicBlock(32, 64)\n",
+    "block(dummy).shape\n",
+    "print(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BottleNeckBlock(\n",
+      "  (blocks): Sequential(\n",
+      "    (0): Sequential(\n",
+      "      (0): Conv2dAuto(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): Sequential(\n",
+      "      (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (3): ReLU(inplace=True)\n",
+      "    (4): Sequential(\n",
+      "      (0): Conv2dAuto(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (activation_fn): ReLU(inplace=True)\n",
+      "  (shortcut): Sequential(\n",
+      "    (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "dummy = torch.ones((1, 32, 10, 10))\n",
+    "\n",
+    "block = BottleNeckBlock(32, 64)\n",
+    "block(dummy).shape\n",
+    "print(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 128, 24, 24])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy = torch.ones((1, 64, 48, 48))\n",
+    "\n",
+    "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n",
+    "layer(dummy).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(64, 128), (128, 256), (256, 512)]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "blocks_sizes=[64, 128, 256, 512]\n",
+    "list(zip(blocks_sizes, blocks_sizes[1:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e = Encoder(depths=[1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1           [-1, 32, 15, 15]             800\n",
+      "       BatchNorm2d-2           [-1, 32, 15, 15]              64\n",
+      "              ReLU-3           [-1, 32, 15, 15]               0\n",
+      "         MaxPool2d-4             [-1, 32, 8, 8]               0\n",
+      "        Conv2dAuto-5             [-1, 32, 8, 8]           9,216\n",
+      "       BatchNorm2d-6             [-1, 32, 8, 8]              64\n",
+      "              ReLU-7             [-1, 32, 8, 8]               0\n",
+      "              ReLU-8             [-1, 32, 8, 8]               0\n",
+      "        Conv2dAuto-9             [-1, 32, 8, 8]           9,216\n",
+      "      BatchNorm2d-10             [-1, 32, 8, 8]              64\n",
+      "             ReLU-11             [-1, 32, 8, 8]               0\n",
+      "             ReLU-12             [-1, 32, 8, 8]               0\n",
+      "       BasicBlock-13             [-1, 32, 8, 8]               0\n",
+      "    ResidualLayer-14             [-1, 32, 8, 8]               0\n",
+      "           Conv2d-15             [-1, 64, 4, 4]           2,048\n",
+      "      BatchNorm2d-16             [-1, 64, 4, 4]             128\n",
+      "       Conv2dAuto-17             [-1, 64, 4, 4]          18,432\n",
+      "      BatchNorm2d-18             [-1, 64, 4, 4]             128\n",
+      "             ReLU-19             [-1, 64, 4, 4]               0\n",
+      "             ReLU-20             [-1, 64, 4, 4]               0\n",
+      "       Conv2dAuto-21             [-1, 64, 4, 4]          36,864\n",
+      "      BatchNorm2d-22             [-1, 64, 4, 4]             128\n",
+      "             ReLU-23             [-1, 64, 4, 4]               0\n",
+      "             ReLU-24             [-1, 64, 4, 4]               0\n",
+      "       BasicBlock-25             [-1, 64, 4, 4]               0\n",
+      "    ResidualLayer-26             [-1, 64, 4, 4]               0\n",
+      "================================================================\n",
+      "Total params: 77,152\n",
+      "Trainable params: 77,152\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.00\n",
+      "Forward/backward pass size (MB): 0.43\n",
+      "Params size (MB): 0.29\n",
+      "Estimated Total Size (MB): 0.73\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary(e, (1, 28, 28), device=\"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resnet = ResidualNetwork(1, 80, activation=\"selu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1           [-1, 32, 15, 15]             800\n",
+      "       BatchNorm2d-2           [-1, 32, 15, 15]              64\n",
+      "              SELU-3           [-1, 32, 15, 15]               0\n",
+      "         MaxPool2d-4             [-1, 32, 8, 8]               0\n",
+      "        Conv2dAuto-5             [-1, 32, 8, 8]           9,216\n",
+      "       BatchNorm2d-6             [-1, 32, 8, 8]              64\n",
+      "              SELU-7             [-1, 32, 8, 8]               0\n",
+      "              SELU-8             [-1, 32, 8, 8]               0\n",
+      "        Conv2dAuto-9             [-1, 32, 8, 8]           9,216\n",
+      "      BatchNorm2d-10             [-1, 32, 8, 8]              64\n",
+      "             SELU-11             [-1, 32, 8, 8]               0\n",
+      "             SELU-12             [-1, 32, 8, 8]               0\n",
+      "       BasicBlock-13             [-1, 32, 8, 8]               0\n",
+      "       Conv2dAuto-14             [-1, 32, 8, 8]           9,216\n",
+      "      BatchNorm2d-15             [-1, 32, 8, 8]              64\n",
+      "             SELU-16             [-1, 32, 8, 8]               0\n",
+      "             SELU-17             [-1, 32, 8, 8]               0\n",
+      "       Conv2dAuto-18             [-1, 32, 8, 8]           9,216\n",
+      "      BatchNorm2d-19             [-1, 32, 8, 8]              64\n",
+      "             SELU-20             [-1, 32, 8, 8]               0\n",
+      "             SELU-21             [-1, 32, 8, 8]               0\n",
+      "       BasicBlock-22             [-1, 32, 8, 8]               0\n",
+      "    ResidualLayer-23             [-1, 32, 8, 8]               0\n",
+      "           Conv2d-24             [-1, 64, 4, 4]           2,048\n",
+      "      BatchNorm2d-25             [-1, 64, 4, 4]             128\n",
+      "       Conv2dAuto-26             [-1, 64, 4, 4]          18,432\n",
+      "      BatchNorm2d-27             [-1, 64, 4, 4]             128\n",
+      "             SELU-28             [-1, 64, 4, 4]               0\n",
+      "             SELU-29             [-1, 64, 4, 4]               0\n",
+      "       Conv2dAuto-30             [-1, 64, 4, 4]          36,864\n",
+      "      BatchNorm2d-31             [-1, 64, 4, 4]             128\n",
+      "             SELU-32             [-1, 64, 4, 4]               0\n",
+      "             SELU-33             [-1, 64, 4, 4]               0\n",
+      "       BasicBlock-34             [-1, 64, 4, 4]               0\n",
+      "       Conv2dAuto-35             [-1, 64, 4, 4]          36,864\n",
+      "      BatchNorm2d-36             [-1, 64, 4, 4]             128\n",
+      "             SELU-37             [-1, 64, 4, 4]               0\n",
+      "             SELU-38             [-1, 64, 4, 4]               0\n",
+      "       Conv2dAuto-39             [-1, 64, 4, 4]          36,864\n",
+      "      BatchNorm2d-40             [-1, 64, 4, 4]             128\n",
+      "             SELU-41             [-1, 64, 4, 4]               0\n",
+      "             SELU-42             [-1, 64, 4, 4]               0\n",
+      "       BasicBlock-43             [-1, 64, 4, 4]               0\n",
+      "    ResidualLayer-44             [-1, 64, 4, 4]               0\n",
+      "          Encoder-45             [-1, 64, 4, 4]               0\n",
+      "           Reduce-46                   [-1, 64]               0\n",
+      "           Linear-47                   [-1, 80]           5,200\n",
+      "          Decoder-48                   [-1, 80]               0\n",
+      "================================================================\n",
+      "Total params: 174,896\n",
+      "Trainable params: 174,896\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.00\n",
+      "Forward/backward pass size (MB): 0.65\n",
+      "Params size (MB): 0.67\n",
+      "Estimated Total Size (MB): 1.32\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary(resnet, (1, 28, 28), device=\"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb
index a68b418..8648afb 100644
--- a/src/notebooks/01-look-at-emnist.ipynb
+++ b/src/notebooks/01-look-at-emnist.ipynb
@@ -31,12 +31,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset = EmnistDataset()\n",
-    "dataset.load_emnist_dataset()"
+    "dataset = EmnistDataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Tensor"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(dataset.data)"
    ]
   },
   {